If there’s one figure that should cause banks to reconsider their household targeting efforts, it’s this: 4,600. That’s the number of bank branches now dotting the American landscape, and it’s less than one-third of the 14,500 that operated in 1984 when the first millennials were learning to count.

Now consider why that is.

Competition, acquisition, digital migration. Fewer large institutions are controlling more of today’s mobile-enabled market, while online-only banks are picking up the deposits of younger households. Being local no longer represents a competitive edge: Regional and community lenders, despite operating 50% of all bank branches, recorded just 20% of deposit growth in the past three years*.

What has not changed, however, is the demand for personalization that is the cornerstone of community banking.

Achieving that personalization today, particularly among digital-adapting bank customers, does not require a big-bank budget, however. It’s just a matter of making those locations the starting point of a fresh approach to segmentation; the kind that makes connecting with customers more of a certainty.

Banks can do this with a fairly practical formula that combines two parts segmentation with one part hyper-personalization. Ready to start?

Two Parts SegmentationFirst – Market Segmentation (Getting to Prospects)
Market segmentation, also known as clustering, requires organizing branch trade areas into manageable, similar-looking groups. We do this by applying a statistical method that combines the branch trading areas that have market similarities. It takes three actions:

Define trade areas and data. The trade areas should constitute the vast majority of a branch’s customers and prospects. A combination of data, such as SourceLink’s accuLink® consumer file of 270 million individual records, as well as aggregated variables from branch-level data, helps shape these areas.

Rank the data variables. Next, banks score the data gathered, based on the mean variable of prospects and customers within each specified market, to rank the attractiveness of the branch trade areas. In some cases, this could mean thousands of potential variables.

Start clustering. The market trade areas are then further narrowed into groups based on their similarities and differences. This process is essential for establishing a manageable number of relatively similar cluster markets.

Second – Audience Segmentation (Getting to Profit)
The bank next wants to further organize each cluster market to distinguish those with the highest likelihood of being profitable prospects. We suggest a two-step modeling process that balances the data and implementation costs against its own effectiveness, resulting in an optimized return on marketing dollars.

This modeling process should be applied to each cluster group:

The Activity Model rank-orders prospects by their propensity to respond and open accounts.

The Balance Model classifies each prospect’s capacity to generate deposits, predicting the average balance of the account after six months.

By applying the results of these combined models into a predetermined targeting matrix, a bank can isolate quadrants of the most profitable prospect segments across mass, mass affluent and ultra-affluent audiences for each unique market area.

One Part Hyper-Personalization: Engage, Consider, Convert
In the realm of digital banking, standard personalization (a person’s name in a salutation, for example), is expected; it’s not enough to stand apart. Customer encounters have to be hyper-personalized to compensate for less-frequent human interactions.

Reliable data insights will help do the trick; it’s just a matter of knowing which tools to use, and how.
Banks can engage customers, for example, by matching direct marketing lists into online platforms such as Google and Facebook. To expand the universe of desirable prospects, banks can also parlay the behavioral data offered by these online platforms into look-a-like prospecting pools.

These direct marketing lists and look-a-like prospect pools offer enough insight to develop the kinds of hyper-personalized digital offers and creative that capture the prospect’s attention and consideration. The banks also can use real-time trigger marketing to identify people who have used these platforms to research checking accounts and send them personalized digital offers for the appropriate checking services.

To move hand-raisers to the next steps of their purchase journeys, banks can continually reach out to prospects who have expressed interest in their offers, perhaps by clicking through to a website or landing page, but who have not yet opened an account. Such retargeting can come in the form of increased digital ads to personal phone calls from a branch manager (for affluent prospects).

Segmenting and Personalizing for Profit
A high number of brick-and-mortar branches may not influence new customer growth as they once did, but digital operations and technologies that support these branches can more than compensate to exceed your 2020 growth goals.

When we use this segmenting and hyper-personalization approach, it consistently delivers a marketing return on investment ranging from 390% to 660%. That’s in just the first year of a new deposit-customer relationship.

Written by: David Funsten – SourceLink’s Vice President of Financial Services Strategy. David has over 25 years’ experience in database marketing and customer relationship management, with a focus on direct marketing and omni-channel programs for retail banks and lenders. Reach out to David at dfunsten@sourcelink.com.